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The Advanced Electrolyte Model (AEM) is a cutting-edge molecular simulation tool designed to optimize electrolyte chemistry. Providing over 100 metrics per test run, this virtual laboratory allows researchers to delve into genome-level properties of electrolytes. Its extensive database includes more than 50 solvents and 30 salts, aiding users in predicting and refining electrolyte characteristics efficiently. AEM accelerates battery research, supporting the shift from fossil fuels to more sustainable energy storage solutions.
CellSage is an advanced platform developed for detailed modeling, simulation, and analysis of battery health. Based on extensive research, it uses hybrid modeling techniques to offer insights into battery performance and longevity under real-world conditions. The tool caters to industries where precise diagnostics are crucial, ensuring optimal management of battery resources by predicting aging and failure trends in various applications like electric vehicles and consumer electronics.
Sentinel Motion for Rail is a sophisticated IoT-based sensor system crafted to monitor critical railroad infrastructure. Initially designed for helicopter gearboxes, this technology provides real-time assessment of track and bearing conditions using a network of smart sensors. By combining traditional detection methods into a single, integrated unit, Sentinel Motion reduces maintenance costs by preventing failure through proactive monitoring and alert systems. This solution is vital for reducing operational expenses in the railroad industry.
ARULE stands at the forefront of predictive analytics, offering a versatile kernel that delivers prognostic estimates like Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH) for systems. Utilizing condition-based data alongside advanced prediction techniques, ARULE enables early intervention by providing accurate RUL and SoH assessments based on real-time data. This essential tool helps industries significantly reduce maintenance costs, avoid unexpected downtime, and enhance system reliability.
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